Nuclear Chart plotter¶
Created on 05 November 2019
@author: Jonas Karthein
@contact: jonas.karthein@cern.ch
@license: MIT license
References¶
[1]: W.J. Huang, et al. Chinese Phys. C 41, 030002 (2017).
[2]: Linguistics Pro Font (= Utopia Font incl. math symbols)
Application¶
The code was used for the dissertation of Jonas Karthein and for several scientific presentations.
Introduction¶
The following code was written to provide publication-ready plots of the nuclear chart, based on the Atomic Mass Evaluation 2016 [1].
Required software and libraries¶
The following code was written in Python 3.7. The required libraries are listed below with a rough description for their task in the code. It doesn't claim to be a full description of the library.
- pandas (data storage and calculation)
- numpy (calculation)
- matplotlib (plotting)
- jupyter (Python notebook environment)
All packages can be fetched using pip:
In [1]:
!pip3 install pandas numpy matplotlib jupyter
In [2]:
import pandas as pd
import numpy as np
from scipy.constants import physical_constants
m_p = physical_constants['proton mass energy equivalent in MeV'][0]
m_n = physical_constants['neutron mass energy equivalent in MeV'][0]
# loading the AME16 mass evaluation table
ame_df = pd.read_fwf('bin/ame16.txt',
skiprows=39,
names=['new_el', 'N_Z', 'N', 'Z', 'A', 'el', 'o', 'ME_keV',
'ME_unc_keV', 'BE_keV',
'BE_unc_keV', 'B', 'Q_keV',
'Q_unc_keV', 'mass_micro_u',
'mass_unc_micro_u'],
widths=[1,3,5,5,5,4,4,15,11,12,7,3,11,9,17,11])
# loading the NUBASE16 table including information about isomers
nubase_df = pd.read_fwf('bin/nubase16.txt',
names=['A', 'Z_+', 'el', 'ME_keV',
'ME_unc_keV', 'Ex_keV',
'Ex_unc_keV', 'Ex_origin', 't_1_2', 't_1_2_unit', 't_1_2_unc', 'JP', 'T',
'ensdf', 'ref', 'yr', 'decay'],
widths=[4,7,7,11,9,10,8,4,9,3,7,8,6,3,9,5,77])
# adding necessary info like the proton number Z, neutron number N, and the year of discovery for plotting
nubase_df['Z'] = nubase_df['Z_+'].str[0:3].astype(int)
nubase_df['N'] = nubase_df.A - nubase_df.Z
nubase_df.yr = nubase_df.yr.fillna(9999).astype(int) # not discovered yet -> year = 9999
nubase_df = nubase_df.set_index('el')
# marking the extrapolated values, removing the '#' and '*' symbols and converting all values into floats
ame_df['extrapol'] = (ame_df.ME_keV.str[-1] == '#')
ame_df = ame_df.replace('#', '.0', regex=True)
ame_df = ame_df.replace('*', np.nan)
ame_df[['ME_keV','ME_unc_keV','BE_keV','BE_unc_keV','Q_keV',
'Q_unc_keV','mass_micro_u','mass_unc_micro_u']] = ame_df[['ME_keV','ME_unc_keV','BE_keV',
'BE_unc_keV','Q_keV','Q_unc_keV',
'mass_micro_u','mass_unc_micro_u'
]].apply(pd.to_numeric)
nubase_df['extrapol'] = (nubase_df.ME_keV.str[-1] == '#')
nubase_df = nubase_df.replace('#', '.0', regex=True)
nubase_df[['ME_keV','ME_unc_keV','Ex_keV','Ex_unc_keV']] = nubase_df[['ME_keV','ME_unc_keV','Ex_keV',
'Ex_unc_keV']].apply(pd.to_numeric, errors='coerce')
# read ISOLDE yield info
import xml.etree.ElementTree as ET
import re
isolde_yields = {}
for child in ET.parse('bin/Isolde-nuclide-chart-bigger.xml').getroot():
el = child.attrib['id']
yields = child[2].text
isolde_yields[re.findall('\d+', el)[0]+re.findall('[A-Za-z]+', el)[0]] = yields
nubase_df['isolde_yields'] = pd.Series(isolde_yields)
nubase_df['isolde_yields_log'] = np.log(nubase_df.isolde_yields.astype(float))
ame_df['mass_prec'] = np.log(abs(np.log(ame_df.mass_unc_micro_u[(ame_df.extrapol == False)] /
ame_df.mass_micro_u[(ame_df.extrapol == False)])))
nubase_df['mass_prec'] = nubase_df.ME_unc_keV / (nubase_df.Z*m_p*1000 + nubase_df.N*m_n*1000 - nubase_df.ME_keV)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.extrapol == False)&(nubase_df.t_1_2_unit.str[0:3].isin(['min','s', 'ms', 'us', 'ns', 'as']))].mass_prec.nsmallest(10)
Out[2]:
General chart -- year of discovery¶
In [3]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
# only measured values of ground states
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\beta^-$', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\beta^+$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='EC', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D935', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\alpha$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC00', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='n', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='SF', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='measured', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='p', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
# displaying extrapolated values of ground states
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#00A2FF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FFCC00', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#16E7CF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D55', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
plt.xticks(magic_nrs, magic_nrs)
plt.yticks(magic_nrs, magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(labelsize=16)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[2],handles[3],handles[4],handles[6],handles[5],handles[8]]
handles2 = [handles[0],handles[7],handles[9]]
lg1 = ax.legend(handles=handles1,loc=4,title = 'Decay', markerscale=6,fontsize=16, ncol=2, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
lg2 = ax.legend(handles=handles2,loc=2,fontsize=16, markerscale=6, ncol=1, edgecolor='white')
plt.setp(lg2.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
ISOLDE yields¶
In [4]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:bold',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
plt.tick_params(labelsize=16)
# measured masses
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='measured', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
# extrapolated masses
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k', zorder=3)
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# plot yield with a colormap in a logarithmic fashion
sc = ax.scatter(x=nubase_df.N.tolist(), y=nubase_df.Z.tolist(), c=nubase_df.isolde_yields_log.tolist(),
s=7, marker='s', edgecolors='k', linewidths=.2, cmap='rainbow',zorder=6)
# inserting the color map
ax2 = f.add_axes([0.78, 0.18, 0.03, 0.4])
cbar = plt.colorbar(sc, cax=ax2, ticks=[np.log(6e-04),np.log(4e+00),np.log(4e+04),np.log(4e+08),np.log(2e+12)])
ax2.set_ylabel('ISOLDE yield / $\mu$C', fontsize=20, fontweight='bold', labelpad=-80)
cbar.ax.set_yticklabels(['$10^{-4}$','$10^{0}$','$10^{4}$','$10^{8}$','$10^{12}$'])
plt.tick_params(axis='both', which='major', labelsize=16)
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
ax.set_xticks(magic_nrs)
ax.set_yticks(magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
lg = ax.legend(fontsize=16, markerscale=6, loc=2, ncol=1, edgecolor='white')
plt.setp(lg.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-isolde-yield.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
Isotopes measured/published at ISOLTRAP since 2017¶
In [5]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
plt.tick_params(labelsize=16)
# loglog-plot of mass uncertainty
sc_m_unc = ax.scatter(x=ame_df.N.tolist(), y=ame_df.Z.tolist(), c=ame_df.mass_prec.tolist(),
s=7, marker='s', edgecolors='k', linewidths=.2, cmap='rainbow',zorder=3)
# plotting the stable and extrapolated isotopes
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='x', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.5, markeredgecolor='k', zorder=3)
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='measured', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k', zorder=0)
ame_df[ame_df.extrapol == True].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k',zorder=2)
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# lists of measured or published masses since 2017 at the ISOLTRAP setup at CERN/ISOLDE
masses_2017__ = ['39K', '85Rb', '133Cs', # offline source
'88Sr', '88Rb', # 2017-05 ArKr
'127Cd', '129Cd', '130Cd', '131Cd', '132Cd', # 2017-07 Cd
'131Cs', # 2017-07 Cs
'48Ar', '98Kr', '99Kr', # 2017-08 ArKr
'49Sc', '50Sc', '51Sc', '99In', '100In', '101In', # 2018-04 ScIn
'70As', '71As', '71Br', '72Br', '73Br', '81Br'] # 2018-05 Br
publications_2017__ = ['27Na', '97Rb', '99Rb', '100Rb', '100Sr', '103Sr', '129Cd', # R. Wolf - Nucl.Instrum. Meth. B 376, 275-280 (2016)
'132Cs', '146Cs', '147Cs', '148Cs', # D. Atanasov - J. Phys. G 44, 064008 (2017)
'185Au', '197At', '219At', '180Au', '188Au', '190Au', # V. Manea - Phys. Rev. C 95, 054322 (2017)
'100Sr', '101Sr', '102Sr', '100Rb', '101Rb', '102Rb', # A. de Roubin - Phys. Rev. C 96, 014310 (2017)
'202Pb', '202Tl', '203Tl', # A. Welker - Eur. Phys. J. A 53, 153 (2017)
'75Cu', '76Cu', '77Cu', '78Cu', '79Cu', # A. Welker - Phys. Rev. Lett. 119, 192502 (2017)
'195Po', '196Po', '197Po', '199Po', '203Po', '208Po', # N. Althubiti - Phys. Rev. C 96, 044325 (2017)
'58Cr', '59Cr', '60Cr', '61Cr', '62Cr', '63Cr', # M. Mougeot - Phys. Rev. Lett. 120, 232501 (2018)
'177Hg', '178Hg', '179Hg', '180Hg', '181Hg', '182Hg', '183Hg', '184Hg', '185Hg', # B. Marsh - Nature Physics 14, 1163 (2018)
'131Cs', '131Xe', # J. Karthein - Hyperfine Interact (2019) 240: 61
'52Cr', '53Cr', '54Cr', '55Cr', '56Cr', '57Cr', '55Mn', '56Fe', '59Fe', '59Co', '75Ga', '77Ga', '78Ga', '79Ga', '140Ce', '140Nd', '156Dy', '160Yb', '168Lu', '178Yb', # W.J. Huang - Eur. Phys. J. A (2019) 55: 96.
'33Mg', '34Mg', '34Si', '34Al', # P. Ascher - Phys. Rev. C 100, 014304 (2019)
'21Na', '21Ne', '23Mg', '23Na'] # J. Karthein - Phys. Rev. C 100, 015502 (2019)
# plotting those measured masses
ax.plot([i[0] for i in [nubase_df.N[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
[i[0] for i in [nubase_df.Z[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
marker='+', c='#FF2D55', lw=0, ms=3, mew=1, zorder=50, label='ISOLTRAP\nsince 2017')
# inserting the color map
ax2 = f.add_axes([0.78, 0.18, 0.03, 0.4])
cbar = plt.colorbar(sc_m_unc, cax=ax2, ticks=[np.log(abs(np.log(3e-11))),np.log(abs(np.log(1e-8))),
np.log(abs(np.log(1e-6))),np.log(abs(np.log(3e-5)))])
ax2.set_ylabel('Precision $\delta$m/m', fontsize=20, fontweight='bold', labelpad=-80)
cbar.ax.set_yticklabels(['$10^{-10}$','$10^{-8}$','$10^{-6}$','$10^{-4}$'])
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
ax.set_xticks(magic_nrs)
ax.set_yticks(magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(axis='both', which='major', labelsize=16)
lg = ax.legend(fontsize=16, markerscale=6, loc=2, ncol=1, edgecolor='white')
plt.setp(lg.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-ISOLTRAP-2017--.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
Nuclear chart: mass precision¶
In [6]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
# only measured values of ground states
limit = 100
limit2 = 10
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^-$', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^+$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='EC', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D935', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\alpha$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC00', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='n', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='SF', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 10$ keV)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='p', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
# displaying [limit2]<x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D93570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0070', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 100$ keV)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.77,s='70%', fontweight='bold', fontsize=8)
# displaying x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0040', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.715,s='40%', fontweight='bold', fontsize=8)
# displaying extrapolated values of ground states
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#00A2FF25', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FFCC0025', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#16E7CF25', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
plt.xticks(magic_nrs, magic_nrs)
plt.yticks(magic_nrs, magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(labelsize=16)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[2],handles[3],handles[4],handles[6],handles[5],handles[8]]
handles2 = [handles[0],handles[7],handles[9],handles[10]]
lg1 = ax.legend(handles=handles1,loc=4,title = 'Decay', markerscale=6,fontsize=16, ncol=2, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
lg2 = ax.legend(handles=handles2,loc=2,fontsize=16, markerscale=6, ncol=1, edgecolor='white')
plt.setp(lg2.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-precision.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
Nuclear chart: mass precision (exp) + ISOLTRAP¶
In [7]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
# only measured values of ground states
limit = 100
limit2 = 10
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^-$', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^+$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='EC', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D935', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\alpha$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC00', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='n', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='SF', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 10$ keV)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='p', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
# displaying [limit2]<x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D93570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0070', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 100$ keV)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.77,s='70%', fontweight='bold', fontsize=8)
# displaying x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0040', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.715,s='40%', fontweight='bold', fontsize=8)
# # displaying extrapolated values of ground states
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#00A2FF25', markeredgewidth=0.1, markeredgecolor='k')
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FFCC0025', markeredgewidth=0.1, markeredgecolor='k')
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#16E7CF25', markeredgewidth=0.1, markeredgecolor='k')
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k')
# nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# lists of measured or published masses since 2017 at the ISOLTRAP setup at CERN/ISOLDE
masses_2017__ = ['39K', '85Rb', '133Cs', # offline source
'88Sr', '88Rb', # 2017-05 ArKr
'127Cd', '129Cd', '130Cd', '131Cd', '132Cd', # 2017-07 Cd
'131Cs', # 2017-07 Cs
'48Ar', '98Kr', '99Kr', # 2017-08 ArKr
'49Sc', '50Sc', '51Sc', '99In', '100In', '101In', # 2018-04 ScIn
'70As', '71As', '71Br', '72Br', '73Br', '81Br'] # 2018-05 Br
publications_2017__ = ['27Na', '97Rb', '99Rb', '100Rb', '100Sr', '103Sr', '129Cd', # R. Wolf - Nucl.Instrum. Meth. B 376, 275-280 (2016)
'132Cs', '146Cs', '147Cs', '148Cs', # D. Atanasov - J. Phys. G 44, 064008 (2017)
'185Au', '197At', '219At', '180Au', '188Au', '190Au', # V. Manea - Phys. Rev. C 95, 054322 (2017)
'100Sr', '101Sr', '102Sr', '100Rb', '101Rb', '102Rb', # A. de Roubin - Phys. Rev. C 96, 014310 (2017)
'202Pb', '202Tl', '203Tl', # A. Welker - Eur. Phys. J. A 53, 153 (2017)
'75Cu', '76Cu', '77Cu', '78Cu', '79Cu', # A. Welker - Phys. Rev. Lett. 119, 192502 (2017)
'195Po', '196Po', '197Po', '199Po', '203Po', '208Po', # N. Althubiti - Phys. Rev. C 96, 044325 (2017)
'58Cr', '59Cr', '60Cr', '61Cr', '62Cr', '63Cr', # M. Mougeot - Phys. Rev. Lett. 120, 232501 (2018)
'177Hg', '178Hg', '179Hg', '180Hg', '181Hg', '182Hg', '183Hg', '184Hg', '185Hg', # B. Marsh - Nature Physics 14, 1163 (2018)
'131Cs', '131Xe', # J. Karthein - Hyperfine Interact (2019) 240: 61
'52Cr', '53Cr', '54Cr', '55Cr', '56Cr', '57Cr', '55Mn', '56Fe', '59Fe', '59Co', '75Ga', '77Ga', '78Ga', '79Ga', '140Ce', '140Nd', '156Dy', '160Yb', '168Lu', '178Yb', # W.J. Huang - Eur. Phys. J. A (2019) 55: 96.
'33Mg', '34Mg', '34Si', '34Al', # P. Ascher - Phys. Rev. C 100, 014304 (2019)
'21Na', '21Ne', '23Mg', '23Na'] # J. Karthein - Phys. Rev. C 100, 015502 (2019)
# plotting those measured masses
ax.plot([i[0] for i in [nubase_df.N[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
[i[0] for i in [nubase_df.Z[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
marker='P', c='yellow', lw=0, zorder=50, label='ISOLTRAP\nsince 2017',
markersize=4, markerfacecolor='yellow', markeredgewidth=0.1, markeredgecolor='k')
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
plt.xticks(magic_nrs, magic_nrs)
plt.yticks(magic_nrs, magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(labelsize=16)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[2],handles[3],handles[4],handles[6],handles[5],handles[8]]
handles2 = [handles[0],handles[7],handles[9],handles[10]]#,handles[11]]
lg1 = ax.legend(handles=handles1,loc=4,title = 'Decay', markerscale=6,fontsize=16, ncol=2, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
lg2 = ax.legend(handles=handles2,loc=2,fontsize=16, markerscale=6, ncol=1, edgecolor='white', framealpha=0)
plt.setp(lg2.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-precision-ISOLTRAP17.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
In [8]:
print('Measured isotopes',len(nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.extrapol == False)]))
print('Extrapolated isotopes',len(nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.extrapol == True)]))
print('Measured isomers',len(nubase_df[(nubase_df['Z_+'].str[3] != '0')&(nubase_df.extrapol == False)]))
Nuclear char: mass precision & r-process path¶
In [9]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
# plot ISOLTRAP measurements (put isoltrap = True)
isoltrap = False
# only measured values of ground states
limit = 100
limit2 = 10
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^-$', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\beta^+$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='EC', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D935', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='$\\alpha$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC00', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='n', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='SF', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 10$ keV/$c^2$)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV < limit2)].plot(x='N', y='Z', ax=ax, label='p', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
# displaying [limit2]<x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D93570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0070', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF70', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF70', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='measured ($\delta m \geq 100$ keV/$c^2$)', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E570', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit2)&(nubase_df.ME_unc_keV < limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5570', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.767,s='70%', fontweight='bold', fontsize=8)
# displaying x<[limit] keV uncertainty
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC0040', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF40', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E540', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)&(nubase_df.ME_unc_keV >= limit)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D5540', alpha=0.5, markeredgewidth=0.1, markeredgecolor='k')
f.text(x=0.16,y=0.71,s='40%', fontweight='bold', fontsize=8)
# displaying extrapolated values of ground states
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#00A2FF25', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FFCC0025', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#16E7CF25', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D5525', markeredgewidth=0.1, markeredgecolor='k')
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# lists of measured or published masses since 2017 at the ISOLTRAP setup at CERN/ISOLDE
masses_2017__ = ['39K', '85Rb', '133Cs', # offline source
'88Sr', '88Rb', # 2017-05 ArKr
'127Cd', '129Cd', '130Cd', '131Cd', '132Cd', # 2017-07 Cd
'131Cs', # 2017-07 Cs
'48Ar', '98Kr', '99Kr', # 2017-08 ArKr
'49Sc', '50Sc', '51Sc', '99In', '100In', '101In', # 2018-04 ScIn
'70As', '71As', '71Br', '72Br', '73Br', '81Br'] # 2018-05 Br
publications_2017__ = ['27Na', '97Rb', '99Rb', '100Rb', '100Sr', '103Sr', '129Cd', # R. Wolf - Nucl.Instrum. Meth. B 376, 275-280 (2016)
'132Cs', '146Cs', '147Cs', '148Cs', # D. Atanasov - J. Phys. G 44, 064008 (2017)
'185Au', '197At', '219At', '180Au', '188Au', '190Au', # V. Manea - Phys. Rev. C 95, 054322 (2017)
'100Sr', '101Sr', '102Sr', '100Rb', '101Rb', '102Rb', # A. de Roubin - Phys. Rev. C 96, 014310 (2017)
'202Pb', '202Tl', '203Tl', # A. Welker - Eur. Phys. J. A 53, 153 (2017)
'75Cu', '76Cu', '77Cu', '78Cu', '79Cu', # A. Welker - Phys. Rev. Lett. 119, 192502 (2017)
'195Po', '196Po', '197Po', '199Po', '203Po', '208Po', # N. Althubiti - Phys. Rev. C 96, 044325 (2017)
'58Cr', '59Cr', '60Cr', '61Cr', '62Cr', '63Cr', # M. Mougeot - Phys. Rev. Lett. 120, 232501 (2018)
'177Hg', '178Hg', '179Hg', '180Hg', '181Hg', '182Hg', '183Hg', '184Hg', '185Hg', # B. Marsh - Nature Physics 14, 1163 (2018)
'131Cs', '131Xe', # J. Karthein - Hyperfine Interact (2019) 240: 61
'52Cr', '53Cr', '54Cr', '55Cr', '56Cr', '57Cr', '55Mn', '56Fe', '59Fe', '59Co', '75Ga', '77Ga', '78Ga', '79Ga', '140Ce', '140Nd', '156Dy', '160Yb', '168Lu', '178Yb', # W.J. Huang - Eur. Phys. J. A (2019) 55: 96.
'33Mg', '34Mg', '34Si', '34Al', # P. Ascher - Phys. Rev. C 100, 014304 (2019)
'21Na', '21Ne', '23Mg', '23Na'] # J. Karthein - Phys. Rev. C 100, 015502 (2019)
# plotting those measured masses
if isoltrap:
ax.plot([i[0] for i in [nubase_df.N[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
[i[0] for i in [nubase_df.Z[nubase_df.index == i].tolist() for i in masses_2017__+publications_2017__]],
marker='P', c='yellow', lw=0, zorder=50, label='ISOLTRAP\nsince 2017',
markersize=4, markerfacecolor='yellow', markeredgewidth=0.1, markeredgecolor='k')
# loading and plotting r-process waiting points, taken from ETFSI-Q
rpath = pd.read_csv('bin/etfsi-r-process-path.csv', header=0)
rpath.plot(x='N', y='Z', ax=ax, label='r-process (ETFSI-Q)', lw=0, marker='s', markersize=2.5, markerfacecolor='#6FF1E900', markeredgewidth=0.5, markeredgecolor='#FF5D00')#C177DA
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
plt.xticks(magic_nrs, magic_nrs)
plt.yticks(magic_nrs, magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(labelsize=16)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[2],handles[3],handles[4],handles[6],handles[5],handles[8]]
if isoltrap:
handles2 = [handles[0],handles[7],handles[9],handles[12],handles[10],handles[11]]
else:
handles2 = [handles[0],handles[7],handles[9],handles[11],handles[10]]
lg1 = ax.legend(handles=handles1,loc=4,title = 'Decay', markerscale=6,fontsize=16, ncol=2, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
lg2 = ax.legend(handles=handles2,loc=2,fontsize=16, markerscale=6, ncol=1, edgecolor='white', framealpha=0)
plt.setp(lg2.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-r-path.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
Carbon cluster coverage¶
In [10]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(9,6))
alpha_c = 0.4
# only measured values of ground states
nubase_df[(nubase_df.t_1_2 == 'stbl')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='stable', lw=0, marker='s', markersize=2.7, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'IS')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='k', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\beta^-$', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\beta^+$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'EC')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='EC', lw=0, marker='s', markersize=2.7, markerfacecolor='#61D935', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'e+')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='$\\alpha$', lw=0, marker='s', markersize=2.7, markerfacecolor='#FFCC00', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'n')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='n', lw=0, marker='s', markersize=2.7, markerfacecolor='#00A2FF', alpha=alpha_c*0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='SF', lw=0, marker='s', markersize=2.7, markerfacecolor='#16E7CF', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='measured', lw=0, marker='s', markersize=2.7, markerfacecolor='#E5E5E5', alpha=alpha_c*0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == False)].plot(x='N', y='Z', ax=ax, label='p', lw=0, marker='s', markersize=2.7, markerfacecolor='#FF2D55', alpha=alpha_c*0.5, markeredgewidth=0.1, markeredgecolor='k')
# displaying extrapolated values of ground states
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B-')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#00A2FF', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'B+')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D55', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'A')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FFCC00', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0:2] == 'SF')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#16E7CF', alpha=alpha_c, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='extrapolated', lw=0, marker='o', markersize=2.7, markerfacecolor='white', alpha=alpha_c*0.5, markeredgewidth=0.1, markeredgecolor='k')
nubase_df[(nubase_df['Z_+'].str[3] == '0')&(nubase_df.decay.str[0] == 'p')&(nubase_df.extrapol == True)].plot(x='N', y='Z', ax=ax, label='_nolegend_', lw=0, marker='o', markersize=2.7, markerfacecolor='#FF2D55', alpha=alpha_c*0.5, markeredgewidth=0.1, markeredgecolor='k')
# marking the mass range of carbon clusters
nubase_df[(nubase_df.A % 12 == 0)].plot(x='N', y='Z', ax=ax, lw=0, marker=[(1,-1), (1.1,-0.9), (-0.9,1.1), (-1,1)], markersize=15, c='#FF2D55', mec=None, label='C cluster')
# inserting lines to display the magic numbers and adding the "magic elements"
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_el = ['He', 'O', 'Ca', 'Ni', 'Sn', 'Pb', 'Ubh']
for i in range(len(magic_nrs)):
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', linewidth=1)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', linewidth=1)
plt.hlines(y=magic_nrs[i], xmin=nubase_df[nubase_df.Z == magic_nrs[i]].N.min()-5, xmax=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.vlines(x=magic_nrs[i], ymin=nubase_df[nubase_df.N == magic_nrs[i]].Z.min()-5, ymax=nubase_df[nubase_df.N == magic_nrs[i]].Z.max()+5, color='k', alpha=0.5, linewidth=1, zorder=50)
plt.text(x=nubase_df[nubase_df.Z == magic_nrs[i]].N.max()+5.5,
y=magic_nrs[i]-2.5,
s='{}'.format(magic_el[i]), fontweight='bold', fontsize=18)
# standardized figure parameters
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('Proton number', fontsize=24, fontweight='bold')
plt.xticks(magic_nrs, magic_nrs)
plt.yticks(magic_nrs, magic_nrs)
ax.set_xlim(-1, nubase_df.N.max()+1)
ax.set_ylim(-1, nubase_df.Z.max()+1)
plt.tick_params(labelsize=16)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[2],handles[3],handles[4],handles[6],handles[5],handles[8]]
handles2 = [handles[0],handles[7],handles[9]]
lg1 = ax.legend(handles=handles1,loc=4,title = 'Decay', markerscale=6,fontsize=16, ncol=2, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
lg2 = ax.legend(handles=handles2,loc=2,fontsize=16, markerscale=6, ncol=1, edgecolor='white')
plt.setp(lg2.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.savefig('nuclear-chart-carbon-clusters.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
$E_B$ plot¶
In [11]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.constants import physical_constants
import warnings
warnings.filterwarnings('ignore')
u = physical_constants['atomic mass constant energy equivalent in MeV'][0]
m_p = physical_constants['proton mass energy equivalent in MeV'][0]
m_n = physical_constants['neutron mass energy equivalent in MeV'][0]
m_e = physical_constants['electron mass energy equivalent in MeV'][0]
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro:bold',
it='Linguistics Pro:italic:bold',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax = plt.subplots(1,1,figsize=(12,6))
# calculating S2N and uncertainties
nubase_df['EB'] = 0.0
nubase_df['EB_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_EB_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
eb_max = []
# plot EB for experimental values
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.EB = -(df_z.ME_keV/1000 + df_z.A * u - (df_z.N * m_n + df_z.Z * m_p + df_z.Z * m_e))/df_z.A
df_z.EB_unc = df_z.ME_unc_keV/1000
eb_max.append([df_z.EB.idxmax(), df_z.N[df_z.EB.idxmax()], df_z.EB.max()])
# save magic-N-S2N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].EB.empty == False:
magic_EB_dict[n].append(df_z[(df_z.N == n)].EB[0])
# plot S2N in black, except for magic-Z -> red
if z in magic_nrs[:3]:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='EB', yerr='EB_unc', ax=ax, zorder=50,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k')
plt.text(x=df_z[(nubase_df.extrapol == False)].N.max(),
y=df_z[(nubase_df.extrapol == False)].EB[df_z[(nubase_df.extrapol == False)].N.idxmax()]-1,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
elif z in magic_nrs[3:]:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='EB', yerr='EB_unc', ax=ax, zorder=50,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k')
plt.text(x=df_z[(nubase_df.extrapol == False)].N.max(),
y=df_z[(nubase_df.extrapol == False)].EB[df_z[(nubase_df.extrapol == False)].N.idxmax()]+0.5,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
else:
try:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='EB', yerr='EB_unc', zorder=1,
ax=ax, label='_nolegend_', lw=0.5,c='k',
marker='o', markersize=1.5, markerfacecolor='k',
markeredgewidth=0.1, markeredgecolor='k')
except:
pass
eb_max = pd.DataFrame(eb_max, columns=['el','N','EB_max'])
eb_max[eb_max.EB_max == eb_max.EB_max.max()].plot(x='N',y='EB_max', ax=ax, c='#FFCC00',
marker='*', label='_nolegend_', markersize=20,
markeredgewidth=0.5, markeredgecolor='k')
plt.text(x=eb_max.N[eb_max.EB_max == eb_max.EB_max.max()]-5,
y=eb_max.EB_max.max()+0.5,
s='$^{62}$Ni', fontweight='bold', fontsize=16)
# inserting lines to display the magic numbers
for n in magic_nrs:
plt.vlines(x=n, ymin=min(magic_EB_dict[n])-1, ymax=max(magic_EB_dict[n])+1,
color='#00A2FF',linewidth=1.5, zorder=1, label='magic $N$')
# standardized figure parameters
plt.tick_params(labelsize=16)
ax.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax.set_ylabel('$E_{B} / (N+Z)$ (MeV)', fontsize=24, fontweight='bold')
ax.set_xticks(magic_nrs)
ax.set_xlim(0,158)
handles, labels = ax.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[0],handles[-1]]
lg1 = ax.legend(handles=handles1,loc=4, markerscale=6,fontsize=16, ncol=1, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
plt.savefig('EB.pdf',bbox_inches='tight',pad_inches=0,transparent=True)
plt.show()
$S_{1n}$ plot¶
In [12]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.constants import physical_constants
import warnings
warnings.filterwarnings('ignore')
# u = physical_constants['atomic mass constant energy equivalent in MeV'][0]
m_p = physical_constants['proton mass energy equivalent in MeV'][0]
m_n = physical_constants['neutron mass energy equivalent in MeV'][0]
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro:bold',
it='Linguistics Pro:italic:bold',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax2 = plt.subplots(1,1,figsize=(12,6))
ax2in = ax2.inset_axes([0.6,0.6,0.4,0.4])
# calculating S1N
nubase_df['s1n'] = 0.0
nubase_df['s1n_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_s1n_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.s1n = df_z.ME_keV.shift(1)/1000 - df_z.ME_keV/1000 + nubase_df.ME_keV['1 n']/1000
df_z.s1n_unc = np.sqrt((df_z.ME_unc_keV.shift(1)/1000).pow(2)
+ (df_z.ME_unc_keV/1000).pow(2)
+ (nubase_df.ME_unc_keV['1 n']/1000)**2)
# save magic-N-S1N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].s1n.empty == False:
magic_s1n_dict[n].append(df_z[df_z.N == n].s1n[0])
# plot S1N in black, except for magic-Z -> red
if z in magic_nrs:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s1n', yerr='s1n_unc', ax=ax2, label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=50)
ax2.text(x=df_z[(nubase_df.extrapol == False)].N[df_z[(nubase_df.extrapol == False)].s1n.idxmin()],
y=df_z[(nubase_df.extrapol == False)].s1n.min()-2,s='$Z$ = {}'.format(z),
fontweight='bold', fontsize=16)
if z == 50:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s1n', ax=ax2in, label='magic $Z$', lw=3, c='#FF2D55', marker='o',
markersize=10, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=50)
else:
try:
df_z[(nubase_df.extrapol == False)&(df_z.Z < 101)].iloc[2:].plot(x='N', y='s1n', ax=ax2, label='_nolegend_', lw=0.5, c='k', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=1)
except:
pass
# inserting lines to display the magic numbers
for magic in magic_nrs:
ax2.axvline(x=magic, color='#00A2FF',linestyle='--', linewidth=1, zorder=1, label='magic $N$')
# standardized figure parameters
ax2.tick_params(labelsize=16)
ax2.minorticks_off()
ax2.get_legend().remove()
ax2.set_ylim(-4.9, 32)
ax2.set_yticks([0,10,20, 30])
# inlet parameters
ax2in.set_xlim(76.5, 85.5)
ax2in.set_ylim(1.5, 8.5)
ax2in.set_xticks([82])
ax2in.set_yticks([2,5,8])
ax2in.set_xlabel('')
ax2in.tick_params(labelsize=16)
ax2in.text(x=77,y=2,s='$Z=50$',fontweight='bold',fontsize=20)
ax2in.axvline(x=82, color='#00A2FF',linestyle='--', linewidth=2, zorder=1)
ax2in.get_legend().remove()
# standardized figure parameters
ax2.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax2.set_ylabel('$S_{1n}$ (MeV)', fontsize=24, fontweight='bold')
ax2.set_xticks(magic_nrs)
ax2.set_xlim(0,158)
handles, labels = ax2.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[0],handles[-1]]
lg1 = ax2.legend(handles=handles1,loc=2, markerscale=6,fontsize=16, ncol=1, edgecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
plt.gca().add_artist(lg1)
plt.savefig('s1n.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
$S_{2n}$ plot¶
In [13]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.constants import physical_constants
import warnings
warnings.filterwarnings('ignore')
# u = physical_constants['atomic mass constant energy equivalent in MeV'][0]
m_p = physical_constants['proton mass energy equivalent in MeV'][0]
m_n = physical_constants['neutron mass energy equivalent in MeV'][0]
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro',
it='Linguistics Pro:italic:bold',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, ax3 = plt.subplots(1,1,figsize=(12,6))
ax3in = ax3.inset_axes([0.6,0.6,0.4,0.4])
# calculating S2N and uncertainties
nubase_df['s2n'] = 0.0
nubase_df['s2n_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_s2n_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
# plot s2n for experimental values
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.s2n = df_z.ME_keV.shift(2)/1000 - df_z.ME_keV/1000 + nubase_df.ME_keV['1 n']/1000 * 2
df_z.s2n_unc = np.sqrt((df_z.ME_unc_keV.shift(2)/1000).pow(2)
+ (df_z.ME_unc_keV/1000).pow(2)
+ (nubase_df.ME_unc_keV['1 n']/1000 * 2)**2)
# save magic-N-S2N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].s2n.empty == False:
magic_s2n_dict[n].append(df_z[(df_z.N == n)].s2n[0])
# plot S2N in black, except for magic-Z -> red
if z in magic_nrs:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s2n', yerr='s2n_unc', ax=ax3,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k', zorder=50)
ax3.text(x=df_z[(nubase_df.extrapol == False)].N.max()-4.3,
y=df_z[(nubase_df.extrapol == False)].s2n[df_z[(nubase_df.extrapol == False)].N.idxmax()]-4,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
if z == 50:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s2n', yerr='s2n_unc', ax=ax3in,
label='magic $Z$', lw=3, c='#FF2D55', marker='o',
markersize=10, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k', zorder=50)
else:
try:
df_z[(nubase_df.extrapol == False)&(df_z.Z < 101)].plot(x='N', y='s2n', yerr='s2n_unc', zorder=1,
ax=ax3, label='_nolegend_', lw=0.5,c='k',
marker='o', markersize=1.5, markerfacecolor='k',
markeredgewidth=0.1, markeredgecolor='k')
except:
pass
# inserting lines to display the magic numbers
for n in magic_nrs:
# ax3.vlines(x=n, ymin=min(magic_s2n_dict[n])-2.5, ymax=max(magic_s2n_dict[n])+2.5,
# color='#00A2FF',linewidth=1.5, zorder=1, label='magic $N$')
ax3.axvline(x=n, color='#00A2FF',linestyle='--', linewidth=1, zorder=1, label='magic $N$')
# areas of sudden change and magic N=32
ellipse1 = mpl.patches.Ellipse(xy=(60, 11), width=4, height=8, edgecolor='#61D935',
fc='None', lw=2, label='sudden change')
ellipse2 = mpl.patches.Ellipse(xy=(91, 13), width=4, height=8, edgecolor='#61D935',
fc='None', lw=2)
ax3.add_patch(ellipse1)
ax3.add_patch(ellipse2)
ax3.vlines(x=32, ymin=7, ymax=13, color='#FFCC00', linewidth=3, label='magic $N=32$')
# standardized figure parameters
ax3.tick_params(labelsize=16)
ax3.minorticks_off()
ax3.set_ylabel('$S_{2n}$ (MeV)', fontsize=24, fontweight='bold')
ax3.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax3.set_xticks(magic_nrs)
ax3.set_xlim(0,158)
ax3.set_ylim(-7.5, 55)
# inlet parameters
ax3in.set_xlim(76.5, 85.5)
ax3in.set_ylim(4, 16)
ax3in.set_xticks([82])
ax3in.set_yticks([5,10,15])
ax3in.set_xlabel('')
ax3in.tick_params(labelsize=16)
ax3in.text(x=77,y=5,s='$Z=50$',fontweight='bold',fontsize=20)
ax3in.axvline(x=82, color='#00A2FF',linestyle='--', linewidth=2, zorder=1)
handles, labels = ax3.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[-1],handles[-7],handles[-8]]
lg1 = ax3.legend(handles=handles1,loc=2, markerscale=6,fontsize=16, ncol=2, edgecolor='white', facecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
for line in lg1.get_lines(): # increase legend linewidth
line.set_linewidth(3)
ax3in.get_legend().remove()
plt.savefig('s2n.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
$E_B, S_{\textit{1n}}, S_{\textit{2n}}$¶
In [14]:
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.constants import physical_constants
import warnings
warnings.filterwarnings('ignore')
u = physical_constants['atomic mass constant energy equivalent in MeV'][0]
m_p = physical_constants['proton mass energy equivalent in MeV'][0]
m_n = physical_constants['neutron mass energy equivalent in MeV'][0]
m_e = physical_constants['electron mass energy equivalent in MeV'][0]
# Utopia LaTeX font with greek letters
mpl.rc('font', family='serif', serif='Linguistics Pro')
mpl.rc('text', usetex=False)
mpl.rc('mathtext', fontset='custom',
rm='Linguistics Pro:bold',
it='Linguistics Pro:italic:bold',
bf='Linguistics Pro:bold')
# plotting the different decay modes
f, (ax1, ax2, ax3) = plt.subplots(3,1,figsize=(12,14),sharex=True,
gridspec_kw={'hspace': 0.02})
################################
# E_B
################################
# calculating S2N and uncertainties
nubase_df['EB'] = 0.0
nubase_df['EB_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_EB_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
eb_max = []
# plot EB for experimental values
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.EB = -(df_z.ME_keV/1000 + df_z.A * u - (df_z.N * m_n + df_z.Z * m_p + df_z.Z * m_e))/df_z.A
df_z.EB_unc = df_z.ME_unc_keV/1000
eb_max.append([df_z.EB.idxmax(), df_z.N[df_z.EB.idxmax()], df_z.EB.max()])
# save magic-N-S2N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].EB.empty == False:
magic_EB_dict[n].append(df_z[(df_z.N == n)].EB[0])
# plot S2N in black, except for magic-Z -> red
if z in magic_nrs[:3]:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='EB', yerr='EB_unc', ax=ax1, zorder=50,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k')
ax1.text(x=df_z[(nubase_df.extrapol == False)].N.max(),
y=df_z[(nubase_df.extrapol == False)].EB[df_z[(nubase_df.extrapol == False)].N.idxmax()]-1,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
elif z in magic_nrs[3:]:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='EB', yerr='EB_unc', ax=ax1, zorder=50,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k')
ax1.text(x=df_z[(nubase_df.extrapol == False)].N.max(),
y=df_z[(nubase_df.extrapol == False)].EB[df_z[(nubase_df.extrapol == False)].N.idxmax()]+0.5,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
else:
try:
df_z[(nubase_df.extrapol == False)&(df_z.Z < 101)].plot(x='N', y='EB', yerr='EB_unc', zorder=1,
ax=ax1, label='_nolegend_', lw=0.5,c='k',
marker='o', markersize=1.5, markerfacecolor='k',
markeredgewidth=0.1, markeredgecolor='k')
except:
pass
eb_max = pd.DataFrame(eb_max, columns=['el','N','EB_max'])
eb_max[eb_max.EB_max == eb_max.EB_max.max()].plot(x='N',y='EB_max', ax=ax1, c='#FFCC00',
marker='*', label='_nolegend_', markersize=20,
markeredgewidth=0.5, markeredgecolor='k')
ax1.text(x=eb_max.N[eb_max.EB_max == eb_max.EB_max.max()]-5,
y=eb_max.EB_max.max()+0.5,
s='$^{62}$Ni', fontweight='bold', fontsize=16)
# inserting lines to display the magic numbers
for n in magic_nrs:
# ax1.vlines(x=n, ymin=min(magic_EB_dict[n])-1, ymax=max(magic_EB_dict[n])+1,
# color='#00A2FF',linewidth=1.5, zorder=1, label='magic $N$')
ax1.axvline(x=n, color='#00A2FF',linestyle='--', linewidth=1, zorder=1, label='magic $N$')
# standardized figure parameters
ax1.tick_params(labelsize=16)
ax1.minorticks_off()
ax1.set_ylabel('$E_{B} / A$ (MeV)', fontsize=24, fontweight='bold')
################################
# s1n
################################
ax2in = ax2.inset_axes([0.75,0.5,0.25,0.5])
# calculating S1N
nubase_df['s1n'] = 0.0
nubase_df['s1n_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_s1n_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.s1n = df_z.ME_keV.shift(1)/1000 - df_z.ME_keV/1000 + nubase_df.ME_keV['1 n']/1000
df_z.s1n_unc = np.sqrt((df_z.ME_unc_keV.shift(1)/1000).pow(2)
+ (df_z.ME_unc_keV/1000).pow(2)
+ (nubase_df.ME_unc_keV['1 n']/1000)**2)
# save magic-N-S1N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].s1n.empty == False:
magic_s1n_dict[n].append(df_z[df_z.N == n].s1n[0])
# plot S1N in black, except for magic-Z -> red
if z in magic_nrs:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s1n', yerr='s1n_unc', ax=ax2, label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=50)
ax2.text(x=df_z[(nubase_df.extrapol == False)].N[df_z[(nubase_df.extrapol == False)].s1n.idxmin()],
y=df_z[(nubase_df.extrapol == False)].s1n.min()-2,s='$Z$ = {}'.format(z),
fontweight='bold', fontsize=16)
if z == 50:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s1n', ax=ax2in, label='magic $Z$', lw=3, c='#FF2D55', marker='o',
markersize=10, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=50)
else:
try:
df_z[(nubase_df.extrapol == False)&(df_z.Z < 101)].iloc[2:].plot(x='N', y='s1n', ax=ax2, label='_nolegend_', lw=0.5, c='k', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1, markeredgecolor='k', zorder=1)
except:
pass
# inserting lines to display the magic numbers
for magic in magic_nrs:
ax2.axvline(x=magic, color='#00A2FF',linestyle='--', linewidth=1, zorder=1, label='magic $N$')
# standardized figure parameters
ax2.tick_params(labelsize=16)
ax2.minorticks_off()
ax2.set_ylabel('$S_{\mathit{1}n}$ (MeV)', fontsize=24, fontweight='bold')
ax2.get_legend().remove()
ax2.set_ylim(-4.9, 32)
ax2.set_yticks([0,10,20, 30])
# inlet parameters
ax2in.set_xlim(76.5, 85.5)
ax2in.set_ylim(1.5, 8.5)
ax2in.set_xticks([82])
ax2in.set_yticks([2,5,8])
ax2in.set_xlabel('')
ax2in.tick_params(labelsize=16)
ax2in.text(x=77,y=2,s='$Z=50$',fontweight='bold',fontsize=20)
ax2in.axvline(x=82, color='#00A2FF',linestyle='--', linewidth=2, zorder=1)
ax2in.get_legend().remove()
################################
# s2n
################################
ax3in = ax3.inset_axes([0.75,0.5,0.25,0.5])
# calculating S2N and uncertainties
nubase_df['s2n'] = 0.0
nubase_df['s2n_unc'] = 0.0
magic_nrs = [2, 8, 20, 28, 50, 82, 126]
magic_s2n_dict = {2:[],8:[],20:[],28:[],50:[],82:[],126:[]}
# plot s2n for experimental values
for z,df_z in nubase_df[(nubase_df['Z_+'].str[3] == '0')].groupby('Z'):
df_z.s2n = df_z.ME_keV.shift(2)/1000 - df_z.ME_keV/1000 + nubase_df.ME_keV['1 n']/1000 * 2
df_z.s2n_unc = np.sqrt((df_z.ME_unc_keV.shift(2)/1000).pow(2)
+ (df_z.ME_unc_keV/1000).pow(2)
+ (nubase_df.ME_unc_keV['1 n']/1000 * 2)**2)
# save magic-N-S2N
for n in magic_nrs:
if df_z[(df_z.N == n)&(nubase_df.extrapol == False)].s2n.empty == False:
magic_s2n_dict[n].append(df_z[(df_z.N == n)].s2n[0])
# plot S2N in black, except for magic-Z -> red
if z in magic_nrs:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s2n', yerr='s2n_unc', ax=ax3,
label='magic $Z$', lw=2, c='#FF2D55', marker='o',
markersize=1.5, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k', zorder=50)
ax3.text(x=df_z[(nubase_df.extrapol == False)].N.max()-4.3,
y=df_z[(nubase_df.extrapol == False)].s2n[df_z[(nubase_df.extrapol == False)].N.idxmax()]-4,
s='$Z$ = {}'.format(z), fontweight='bold', fontsize=16)
if z == 50:
df_z[(nubase_df.extrapol == False)].plot(x='N', y='s2n', yerr='s2n_unc', ax=ax3in,
label='magic $Z$', lw=3, c='#FF2D55', marker='o',
markersize=10, markerfacecolor='k', markeredgewidth=0.1,
markeredgecolor='k',ecolor='k', zorder=50)
else:
try:
df_z[(nubase_df.extrapol == False)&(df_z.Z < 101)].plot(x='N', y='s2n', yerr='s2n_unc', zorder=1,
ax=ax3, label='_nolegend_', lw=0.5,c='k',
marker='o', markersize=1.5, markerfacecolor='k',
markeredgewidth=0.1, markeredgecolor='k')
except:
pass
# inserting lines to display the magic numbers
for n in magic_nrs:
# ax3.vlines(x=n, ymin=min(magic_s2n_dict[n])-2.5, ymax=max(magic_s2n_dict[n])+2.5,
# color='#00A2FF',linewidth=1.5, zorder=1, label='magic $N$')
ax3.axvline(x=n, color='#00A2FF',linestyle='--', linewidth=1, zorder=1, label='magic $N$')
# areas of sudden change and magic N=32
ellipse1 = mpl.patches.Ellipse(xy=(60, 11), width=4, height=8, edgecolor='#61D935',
fc='None', lw=2, label='sudden change')
ellipse2 = mpl.patches.Ellipse(xy=(91, 13), width=4, height=8, edgecolor='#61D935',
fc='None', lw=2)
ax3.add_patch(ellipse1)
ax3.add_patch(ellipse2)
ax3.vlines(x=32, ymin=7, ymax=13, color='#FFCC00', linewidth=3, label='magic $N=32$')
# standardized figure parameters
ax3.tick_params(labelsize=16)
ax3.minorticks_off()
ax3.set_ylabel('$S_{\mathit{2}n}$ (MeV)', fontsize=24, fontweight='bold')
ax3.set_xlabel('Neutron number', fontsize=24, fontweight='bold')
ax3.set_xticks(magic_nrs)
ax3.set_xlim(0,158)
ax3.set_ylim(-7.5, 55)
# inlet parameters
ax3in.set_xlim(76.5, 85.5)
ax3in.set_ylim(4, 16)
ax3in.set_xticks([82])
ax3in.set_xlabel('')
ax3in.tick_params(labelsize=16)
ax3in.text(x=77,y=5,s='$Z=50$',fontweight='bold',fontsize=20)
ax3in.axvline(x=82, color='#00A2FF',linestyle='--', linewidth=2, zorder=1)
handles, labels = ax3.get_legend_handles_labels() # allows splitting the legend
handles1 = [handles[1],handles[-1],handles[-7],handles[-8]]
lg1 = ax1.legend(handles=handles1,loc=4, markerscale=6,fontsize=16, ncol=1, edgecolor='white', facecolor='white')
plt.setp(lg1.get_title(),fontsize=20,fontweight='bold') # legend title bold
for line in lg1.get_lines(): # increase legend linewidth
line.set_linewidth(3)
ax3.get_legend().remove()
ax3in.get_legend().remove()
f.align_ylabels((ax1,ax2,ax3))
plt.savefig('eb_s1n_s2n.pdf',bbox_inches='tight',pad_inches=0)
plt.show()
